Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle

An integrative multi-context Mendelian randomization method for identifying risk genes across human tissues

Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context- or tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers insights into disease mechanisms.

Association analysis of indel variants and gene expression identifies MDM4 as a novel locus for skeletal muscle hypertrophy and power athlete status

Insertions and deletions (indels) are the second most common type of variation in the human genome. However, limited data on their associations with exercise-related phenotypes have been documented. The aim of the present study was to examine the association between 18,370 indel variants and power athlete status, followed by additional studies in 357,246 individuals. In the discovery phase, the D allele of the MDM4 gene rs35493922 I/D polymorphism was over-represented in power athletes compared with control subjects (P = 7.8 × 10-9) and endurance athletes (P = 0.0012). These findings were replicated in independent cohorts, showing a higher D allele frequency in power athletes compared with control subjects (P = 0.016) and endurance athletes (P = 0.031). Furthermore, the D allele was positively associated (P = 0.0013) with greater fat-free mass in the UK Biobank. MDM4 encodes a protein that inhibits the activity of p53, which induces muscle fibre atrophy. Accordingly, we found that MDM4 expression was significantly higher in the vastus lateralis of power athletes compared with endurance athletes (P = 0.0009) and was positively correlated with the percentage of fast-twitch muscle fibres (P = 0.0062) and the relative area occupied by fast-twitch muscle fibres (P = 0.0086). The association between MDM4 gene expression and an increased proportion of fast-twitch muscle fibres was confirmed in two additional cohorts. Finally, we found that the MDM4 DD genotype was associated with increased MDM4 gene expression in vastus lateralis and greater cross-sectional area of fast-twitch muscle fibres. In conclusion, MDM4 is suggested to be a potential regulator of muscle fibre specification and size, with its indel variant being associated with power athlete status. HIGHLIGHTS: What is the central question of this study? Which indel variants are functional and associated with sport- and exercise-related traits? What is the main finding and its importance? Out of 18,370 tested indels, the MDM4 gene rs35493922 I/D polymorphism was found to be the functional variant (affecting gene expression) and the most significant, with the deletion allele showing associations with power athlete status, fat-free mass and cross-sectional area of fast-twitch muscle fibres. Furthermore, the expression of MDM4 was positively correlated with the percentage of fast-twitch muscle fibres and the relative area occupied by fast-twitch muscle fibres.

Single-cell transcriptomic profiling of human pancreatic islets reveals genes responsive to glucose exposure over 24 h

AIMS/HYPOTHESIS

Disruption of pancreatic islet function and glucose homeostasis can lead to the development of sustained hyperglycaemia, beta cell glucotoxicity and subsequently type 2 diabetes. In this study, we explored the effects of in vitro hyperglycaemic conditions on human pancreatic islet gene expression across 24 h in six pancreatic cell types: alpha; beta; gamma; delta; ductal; and acinar. We hypothesised that genes associated with hyperglycaemic conditions may be relevant to the onset and progression of diabetes.

METHODS

We exposed human pancreatic islets from two donors to low (2.8 mmol/l) and high (15.0 mmol/l) glucose concentrations over 24 h in vitro. To assess the transcriptome, we performed single-cell RNA-seq (scRNA-seq) at seven time points. We modelled time as both a discrete and continuous variable to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. Additionally, we integrated genomic features and genetic summary statistics to nominate candidate effector genes. For three of these genes, we functionally characterised the effect on insulin production and secretion using CRISPR interference to knock down gene expression in EndoC-βH1 cells, followed by a glucose-stimulated insulin secretion assay.

RESULTS

In the discrete time models, we identified 1344 genes associated with time and 668 genes associated with glucose exposure across all cell types and time points. In the continuous time models, we identified 1311 genes associated with time, 345 genes associated with glucose exposure and 418 genes associated with interaction effects between time and glucose across all cell types. By integrating these expression profiles with summary statistics from genetic association studies, we identified 2449 candidate effector genes for type 2 diabetes, HbA1c, random blood glucose and fasting blood glucose. Of these candidate effector genes, we showed that three (ERO1B, HNRNPA2B1 and RHOBTB3) exhibited an effect on glucose-stimulated insulin production and secretion in EndoC-βH1 cells.

CONCLUSIONS/INTERPRETATION

The findings of our study provide an in-depth characterisation of the 24 h transcriptomic response of human pancreatic islets to glucose exposure at a single-cell resolution. By integrating differentially expressed genes with genetic signals for type 2 diabetes and glucose-related traits, we provide insights into the molecular mechanisms underlying glucose homeostasis. Finally, we provide functional evidence to support the role of three candidate effector genes in insulin secretion and production.

DATA AVAILABILITY

The scRNA-seq data from the 24 h glucose exposure experiment performed in this study are available in the database of Genotypes and Phenotypes (dbGap; https://www.ncbi.nlm.nih.gov/gap/ ) with accession no. phs001188.v3.p1. Study metadata and summary statistics for the differential expression, gene set enrichment and candidate effector gene prediction analyses are available in the Zenodo data repository ( https://zenodo.org/ ) under accession number 11123248. The code used in this study is publicly available at https://github.com/CollinsLabBioComp/publication-islet_glucose_timecourse .

Molecular adaptations in response to exercise training are associated with tissue-specific transcriptomic and epigenomic signatures

Regular exercise has many physical and brain health benefits, yet the molecular mechanisms mediating exercise effects across tissues remain poorly understood. Here we analyzed 400 high-quality DNA methylation, ATAC-seq, and RNA-seq datasets from eight tissues from control and endurance exercise-trained (EET) rats. Integration of baseline datasets mapped the gene location dependence of epigenetic control features and identified differing regulatory landscapes in each tissue. The transcriptional responses to 8 weeks of EET showed little overlap across tissues and predominantly comprised tissue-type enriched genes. We identified sex differences in the transcriptomic and epigenomic changes induced by EET. However, the sex-biased gene responses were linked to shared signaling pathways. We found that many G protein-coupled receptor-encoding genes are regulated by EET, suggesting a role for these receptors in mediating the molecular adaptations to training across tissues. Our findings provide new insights into the mechanisms underlying EET-induced health benefits across organs.

DNA Methylation in the Adaptive Response to Exercise

Emerging evidence published over the past decade has highlighted the role of DNA methylation in skeletal muscle function and health, including as an epigenetic transducer of the adaptive response to exercise. In this review, we aim to synthesize the latest findings in this field to highlight: (1) the shifting understanding of the genomic localization of altered DNA methylation in response to acute and chronic aerobic and resistance exercise in skeletal muscle (e.g., promoter, gene bodies, enhancers, intergenic regions, un-annotated regions, and genome-wide methylation); (2) how these global/regional methylation changes relate to transcriptional activity following exercise; and (3) the factors (e.g., individual demographic or genetic features, dietary, training history, exercise parameters, local epigenetic characteristics, circulating hormones) demonstrated to alter both the pattern of DNA methylation after exercise, and the relationship between DNA methylation and gene expression. Finally, we discuss the changes in non-CpG methylation and 5-hydroxymethylation after exercise, as well as the importance of emerging single-cell analyses to future studies-areas of increasing focus in the field of epigenetics. We anticipate that this review will help generate a framework for clinicians and researchers to begin developing and testing exercise interventions designed to generate targeted changes in DNA methylation as part of a personalized exercise regimen.

Conserved methylation signatures associate with the tumor immune microenvironment and immunotherapy response

BACKGROUND

Aberrant DNA methylation is a major characteristic of cancer genomes. It remains unclear which biological processes determine epigenetic reprogramming and how these processes influence the variants in the cancer methylome, which can further impact cancer phenotypes.

METHODS

We performed pairwise permutations of 381,900 loci in 569 paired DNA methylation profiles of cancer tissue and matched normal tissue from The Cancer Genome Atlas (TCGA) and defined conserved differentially methylated positions (DMPs) based on the resulting null distribution. Then, we derived independent methylation signatures from 2,465 cancer-only methylation profiles from the TCGA and 241 cell line-based methylation profiles from the Genomics of Drug Sensitivity in Cancer (GDSC) cohort using nonnegative matrix factorization (NMF). We correlated DNA methylation signatures with various clinical and biological features, including age, survival, cancer stage, tumor immune microenvironment factors, and immunotherapy response. We inferred the determinant genes of these methylation signatures by integrating genomic and transcriptomic data and evaluated the impact of these signatures on cancer phenotypes in independent bulk and single-cell RNA/methylome cohorts.

RESULTS

We identified 7,364 differentially methylated positions (2,969 Hyper-DMPs and 4,395 Hypo-DMPs) in nine cancer types from the TCGA. We subsequently retrieved three highly conserved, independent methylation signatures (Hyper-MS1, Hypo-MS1, and Hypo-MS4) from cancer tissues and cell lines based on these Hyper and Hypo-DMPs. Our data suggested that Hypo-MS4 activity predicts poor survival and is associated with immunotherapy response and distant tumor metastasis, and Hypo-MS4 activity is related to TP53 mutation and FOXA1 binding specificity. In addition, we demonstrated a correlation between the activities of Hypo-MS4 in cancer cells and the fractions of regulatory CD4 + T cells with the expression levels of immunological genes in the tumor immune microenvironment.

CONCLUSIONS

Our findings demonstrated that the methylation signatures of distinct biological processes are associated with immune activity in the cancer microenvironment and predict immunotherapy response.

Integrative cross-omics and cross-context analysis elucidates molecular links underlying genetic effects on complex traits

Genetic effects on functionally related 'omic' traits often co-occur in relevant cellular contexts, such as tissues. Motivated by the multi-tissue methylation quantitative trait loci (mQTLs) and expression QTLs (eQTLs) analysis, we propose X-ING (Cross-INtegrative Genomics) for cross-omics and cross-context integrative analysis. X-ING takes as input multiple matrices of association statistics, each obtained from different omics data types across multiple cellular contexts. It models the latent binary association status of each statistic, captures the major association patterns among omics data types and contexts, and outputs the posterior mean and probability for each input statistic. X-ING enables the integration of effects from different omics data with varying effect distributions. In the multi-tissue cis-association analysis, X-ING shows improved detection and replication of mQTLs by integrating eQTL maps. In the trans-association analysis, X-ING reveals an enrichment of trans-associations in many disease/trait-relevant tissues.

An integrative multi-context Mendelian randomization method for identifying risk genes across human tissues

Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context/tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease-relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers new insights into disease mechanisms.

QTL mapping of human retina DNA methylation identifies 87 gene-epigenome interactions in age-related macular degeneration

DNA methylation provides a crucial epigenetic mark linking genetic variations to environmental influence. We have analyzed array-based DNA methylation profiles of 160 human retinas with co-measured RNA-seq and >8 million genetic variants, uncovering sites of genetic regulation in cis (37,453 methylation quantitative trait loci and 12,505 expression quantitative trait loci) and 13,747 DNA methylation loci affecting gene expression, with over one-third specific to the retina. Methylation and expression quantitative trait loci show non-random distribution and enrichment of biological processes related to synapse, mitochondria, and catabolism. Summary data-based Mendelian randomization and colocalization analyses identify 87 target genes where methylation and gene-expression changes likely mediate the genotype effect on age-related macular degeneration. Integrated pathway analysis reveals epigenetic regulation of immune response and metabolism including the glutathione pathway and glycolysis. Our study thus defines key roles of genetic variations driving methylation changes, prioritizes epigenetic control of gene expression, and suggests frameworks for regulation of macular degeneration pathology by genotype-environment interaction in retina.

Variant to gene mapping for carpal tunnel syndrome risk loci implicates skeletal muscle regulatory elements

BACKGROUND

Carpal tunnel syndrome (CTS) is a common disorder caused by compression of the median nerve in the wrist, resulting in pain and numbness throughout the hand and forearm. While multiple behavioural and physiological factors influence CTS risk, a growing body of evidence supports a strong genetic contribution. Recent genome-wide association study (GWAS) efforts have reported 53 independent signals associated with CTS. While GWAS can identify genetic loci conferring risk, it does not determine which cell types drive the genetic aetiology of the trait, which variants are "causal" at a given signal, and which effector genes correspond to these non-coding variants. These obstacles limit interpretation of potential disease mechanisms.

METHODS

We analysed CTS GWAS findings in the context of chromatin conformation between gene promoters and accessible chromatin regions across cellular models of bone, skeletal muscle, adipocytes and neurons. We identified proxy variants in high LD with the lead CTS sentinel SNPs residing in promoter connected open chromatin in the skeletal muscle and bone contexts.

FINDINGS

We detected significant enrichment for heritability in skeletal muscle myotubes, as well as a weaker correlation in human mesenchymal stem cell-derived osteoblasts. In myotubes, our approach implicated 117 genes contacting 60 proxy variants corresponding to 20 of the 53 GWAS signals. In the osteoblast context we implicated 30 genes contacting 24 proxy variants coinciding with 12 signals, of which 19 genes shared. We subsequently prioritized BZW2 as a candidate effector gene in CTS and implicated it as novel gene that perturbs myocyte differentiation in vitro.

INTERPRETATION

Taken together our results suggest that the CTS genetic component influences the size, integrity, and organization of multiple tissues surrounding the carpal tunnel, in particular muscle and bone, to predispose the nerve to being compressed in this disease setting.

FUNDING

This work was supported by NIH Grant UM1 DK126194 (SFAG and WY), R01AG072705 (SFAG & KDH) and the Center for Spatial and Functional Genomics at CHOP (SFAG & ADW). SFAG is supported by the Daniel B. Burke Endowed Chair for Diabetes Research. WY is supported by the Perelman School of Medicine of the University of Pennsylvania.

Common and ethnic-specific derangements in skeletal muscle transcriptome associated with obesity

BACKGROUND

Obesity is a common disease with a higher prevalence among African Americans. Obesity alters cellular function in many tissues, including skeletal muscle, and is a risk factor for many life-threatening diseases, including cardiovascular disease and diabetes. The similarities and differences in molecular mechanisms that may explain ethnic disparities in obesity between African and European ancestry individuals have not been studied.

METHODS

In this study, data from transcriptome-wide analyses on skeletal muscle tissues from well-powered human cohorts were used to compare genes and biological pathways affected by obesity in European and African ancestry populations. Data on obesity-induced differentially expressed transcripts and GWAS-identified SNPs were integrated to prioritize target genes for obesity-associated genetic variants.

RESULTS

Linear regression analysis in the FUSION (European, N = 301) and AAGMEx (African American, N = 256) cohorts identified a total of 2569 body mass index (BMI)-associated transcripts (q < 0.05), of which 970 genes (at p < 0.05) are associated in both cohorts, and the majority showed the same direction of effect on BMI. Biological pathway analyses, including over-representation and gene-set enrichment analyses, identified enrichment of protein synthesis pathways (e.g., ribosomal function) and the ceramide signaling pathway in both cohorts among BMI-associated down- and up-regulated transcripts, respectively. A comparison using the IPA-tool suggested the activation of inflammation pathways only in Europeans with obesity. Interestingly, these analyses suggested repression of the mitochondrial oxidative phosphorylation pathway in Europeans but showed its activation in African Americans. Integration of SNP-to-Gene analyses-predicted target genes for obesity-associated genetic variants (GWAS-identified SNPs) and BMI-associated transcripts suggested that these SNPs might cause obesity by altering the expression of 316 critical target genes (e.g., GRB14) in the muscle.

CONCLUSIONS

This study provides a replication of obesity-associated transcripts and biological pathways in skeletal muscle across ethnicities, but also identifies obesity-associated processes unique in either African or European ancestry populations.

Priority index for critical Covid-19 identifies clinically actionable targets and drugs

While genome-wide studies have identified genomic loci in hosts associated with life-threatening Covid-19 (critical Covid-19), the challenge of resolving these loci hinders further identification of clinically actionable targets and drugs. Building upon our previous success, we here present a priority index solution designed to address this challenge, generating the target and drug resource that consists of two indexes: the target index and the drug index. The primary purpose of the target index is to identify clinically actionable targets by prioritising genes associated with Covid-19. We illustrate the validity of the target index by demonstrating its ability to identify pre-existing Covid-19 phase-III drug targets, with the majority of these targets being found at the leading prioritisation (leading targets). These leading targets have their evolutionary origins in Amniota ('four-leg vertebrates') and are predominantly involved in cytokine-cytokine receptor interactions and JAK-STAT signaling. The drug index highlights opportunities for repurposing clinically approved JAK-STAT inhibitors, either individually or in combination. This proposed strategic focus on the JAK-STAT pathway is supported by the active pursuit of therapeutic agents targeting this pathway in ongoing phase-II/III clinical trials for Covid-19.

Torch-eCpG: a fast and scalable eQTM mapper for thousands of molecular phenotypes with graphical processing units

BACKGROUND

Gene expression may be regulated by the DNA methylation of regulatory elements in cis, distal, and trans regions. One method to evaluate the relationship between DNA methylation and gene expression is the mapping of expression quantitative trait methylation (eQTM) loci (also called expression associated CpG loci, eCpG). However, no open-source tools are available to provide eQTM mapping. In addition, eQTM mapping can involve a large number of comparisons which may prevent the analyses due to limitations of computational resources. Here, we describe Torch-eCpG, an open-source tool to perform eQTM mapping that includes an optimized implementation that can use the graphical processing unit (GPU) to reduce runtime.

RESULTS

We demonstrate the analyses using the tool are reproducible, up to 18 × faster using the GPU, and scale linearly with increasing methylation loci.

CONCLUSIONS

Torch-eCpG is a fast, reliable, and scalable tool to perform eQTM mapping. Source code for Torch-eCpG is available at https://github.com/kordk/torch-ecpg .

Whole genome sequencing of 4,787 individuals identifies gene-based rare variants in age-related macular degeneration

Genome-wide association studies have contributed extensively to the discovery of disease-associated common variants. However, the genetic contribution to complex traits is still largely difficult to interpret. We report a genome-wide association study of 2394 cases and 2393 controls for age-related macular degeneration (AMD) via whole-genome sequencing, with 46.9 million genetic variants. Our study reveals significant single-variant association signals at four loci and independent gene-based signals in CFH, C2, C3, and NRTN. Using data from the Exome Aggregation Consortium (ExAC) for a gene-based test, we demonstrate an enrichment of predicted rare loss-of-function variants in CFH, CFI, and an as-yet unreported gene in AMD, ORMDL2. Our method of using a large variant list without individual-level genotypes as an external reference provides a flexible and convenient approach to leverage the publicly available variant datasets to augment the search for rare variant associations, which can explain additional disease risk in AMD.

Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation

Skeletal muscle, the largest human organ by weight, is relevant to several polygenic metabolic traits and diseases including type 2 diabetes (T2D). Identifying genetic mechanisms underlying these traits requires pinpointing the relevant cell types, regulatory elements, target genes, and causal variants. Here, we used genetic multiplexing to generate population-scale single nucleus (sn) chromatin accessibility (snATAC-seq) and transcriptome (snRNA-seq) maps across 287 frozen human skeletal muscle biopsies representing 456,880 nuclei. We identified 13 cell types that collectively represented 983,155 ATAC summits. We integrated genetic variation to discover 6,866 expression quantitative trait loci (eQTL) and 100,928 chromatin accessibility QTL (caQTL) (5% FDR) across the five most abundant cell types, cataloging caQTL peaks that atlas-level snATAC maps often miss. We identified 1,973 eGenes colocalized with caQTL and used mediation analyses to construct causal directional maps for chromatin accessibility and gene expression. 3,378 genome-wide association study (GWAS) signals across 43 relevant traits colocalized with sn-e/caQTL, 52% in a cell-specific manner. 77% of GWAS signals colocalized with caQTL and not eQTL, highlighting the critical importance of population-scale chromatin profiling for GWAS functional studies. GWAS-caQTL colocalization showed distinct cell-specific regulatory paradigms. For example, a C2CD4A/B T2D GWAS signal colocalized with caQTL in muscle fibers and multiple chromatin loop models nominated VPS13C, a glucose uptake gene. Sequence of the caQTL peak overlapping caSNP rs7163757 showed allelic regulatory activity differences in a human myocyte cell line massively parallel reporter assay. These results illuminate the genetic regulatory architecture of human skeletal muscle at high-resolution epigenomic, transcriptomic, and cell state scales and serve as a template for population-scale multi-omic mapping in complex tissues and traits.

Methyl-TWAS: A powerful method for in silico transcriptome-wide association studies (TWAS) using long-range DNA methylation

In silico transcriptome-wide association studies (TWAS) are commonly used to test whether expression of specific genes is linked to a complex trait. However, genotype-based in silico TWAS such as PrediXcan, exhibit low prediction accuracy for a majority of genes because genotypic data lack tissue- and disease-specificity and are not affected by the environment. Because methylation is tissue-specific and, like gene expression, can be modified by environment or disease status, methylation should predict gene expression with more accuracy than SNPs. Therefore, we propose Methyl-TWAS, the first approach that utilizes long-range methylation markers to impute gene expression for in silico TWAS through penalized regression. Methyl-TWAS 1) predicts epigenetically regulated/associated expression (eGReX), which incorporates tissue-specific expression and both genetically- (GReX) and environmentally-regulated expression to identify differentially expressed genes (DEGs) that could not be identified by genotype-based methods; and 2) incorporates both cis- and trans- CpGs, including various regulatory regions to identify DEGs that would be missed using cis- methylation only. Methyl-TWAS outperforms PrediXcan and two other methods in imputing gene expression in the nasal epithelium, particularly for immunity-related genes and DEGs in atopic asthma. Methyl-TWAS identified 3,681 (85.2%) of the 4,316 DEGs identified in a previous TWAS of atopic asthma using measured expression, while PrediXcan could not identify any gene. Methyl-TWAS also outperforms PrediXcan for expression imputation as well as in silico TWAS in white blood cells. Methyl-TWAS is a valuable tool for in silico TWAS, leveraging a growing body of publicly available genome-wide DNA methylation data for a variety of human tissues.

Genome-wide association study analysis of disease severity in Acne reveals novel biological insights

Acne vulgaris is a common skin disease that affects >85% of teenage young adults among which >8% develop severe lesions that leaves permanent scars. Genetic heritability studies of acne in twin cohorts have estimated that the heritability for acne is 80%. Previous genome-wide association studies (GWAS) have identified 50 genetic loci associated with increased risk of developing acne when compared to healthy individuals. However only a few studies have investigated genetic association with disease severity. GWAS of disease progression may provide a more effective approach to unveil potential disease modifying therapeutic targets. Here, we performed a multi-ethnic GWAS analysis to capture disease severity in acne patients by using individuals with normal acne as a control. Our cohort consists of a total of 2,956 participants, including 290 severe acne cases and 930 normal acne controls from FinnGen, and 522 cases and 1,214 controls from BioVU. We also performed mendelian randomization (MR), colocalization analyses and transcriptome-wide association study (TWAS) to identify putative causal genes. Lastly, we performed gene-set enrichment analysis using MAGMA to implicate biological pathways that drive disease severity in Acne. We identified two new loci associated with acne severity at the genome-wide significance level, six novel associated genes by MR, colocalization and TWAS analyses, including genes CDC7, SLC7A1, ADAM23, TTLL10, CDK20 and DNAJA4 , and 5 novel pathways by MAGMA analyses. Our study suggests that the etiologies of acne susceptibility and severity have limited overlap, with only 26% of known acne risk loci presenting nominal association with acne severity and none of the novel severity associated genes reported as associated with acne risk in previous GWAS.

Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective

Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.

The functional impact of rare variation across the regulatory cascade

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype's ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13-27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, "Watershed," to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer's disease.

Cis- and trans-eQTM analysis reveals novel epigenetic and transcriptomic immune markers of atopic asthma in airway epithelium

BACKGROUND

Expression quantitative trait methylation (eQTM) analyses uncover associations between DNA methylation markers and gene expression. Most eQTM analyses of complex diseases have focused on cis-eQTM pairs (within 1 megabase).

OBJECTIVES

This study sought to identify cis- and trans-methylation markers associated with gene expression in airway epithelium from youth with and without atopic asthma.

METHODS

In this study, the investigators conducted both cis- and trans-eQTM analyses in nasal (airway) epithelial samples from 158 Puerto Rican youth with atopic asthma and 100 control subjects without atopy or asthma. The investigators then attempted to replicate their findings in nasal epithelial samples from 2 studies of children, while also examining whether their results in nasal epithelium overlap with those from an eQTM analysis in white blood cells from the Puerto Rican subjects.

RESULTS

This study identified 9,108 cis-eQTM pairs and 2,131,500 trans-eQTM pairs. Trans-associations were significantly enriched for transcription factor and microRNA target genes. Furthermore, significant cytosine-phosphate-guanine sites (CpGs) were differentially methylated in atopic asthma and significant genes were enriched for genes differentially expressed in atopic asthma. In this study, 50.7% to 62.6% of cis- and trans-eQTM pairs identified in Puerto Rican youth were replicated in 2 smaller cohorts at false discovery rate-adjusted P < .1. Replicated genes in the trans-eQTM analysis included biologically plausible asthma-susceptibility genes (eg, HDC, NLRP3, ITGAE, CDH26, and CST1) and are enriched in immune pathways.

CONCLUSIONS

Studying both cis- and trans-epigenetic regulation of airway epithelial gene expression can identify potential causal and regulatory pathways or networks for childhood asthma. Trans-eQTM CpGs may regulate gene expression in airway epithelium through effects on transcription factor and microRNA target genes.

DNA methylation regulator-mediated modification patterns and risk of intracranial aneurysm: a multi-omics and epigenome-wide association study integrating machine learning, Mendelian randomization, eQTL and mQTL data

BACKGROUND

Intracranial aneurysms (IAs) pose a significant and intricate challenge. Elucidating the interplay between DNA methylation and IA pathogenesis is paramount to identify potential biomarkers and therapeutic interventions.

METHODS

We employed a comprehensive bioinformatics investigation of DNA methylation in IA, utilizing a transcriptomics-based methodology that encompassed 100 machine learning algorithms, genome-wide association studies (GWAS), Mendelian randomization (MR), and summary-data-based Mendelian randomization (SMR). Our sophisticated analytical strategy allowed for a systematic assessment of differentially methylated genes and their implications on the onset, progression, and rupture of IA.

RESULTS

We identified DNA methylation-related genes (MRGs) and associated molecular pathways, and the MR and SMR analyses provided evidence for potential causal links between the observed DNA methylation events and IA predisposition.

CONCLUSION

These insights not only augment our understanding of the molecular underpinnings of IA but also underscore potential novel biomarkers and therapeutic avenues. Although our study faces inherent limitations and hurdles, it represents a groundbreaking initiative in deciphering the intricate relationship between genetic, epigenetic, and environmental factors implicated in IA pathogenesis.

eQTL Catalogue 2023: New datasets, X chromosome QTLs, and improved detection and visualisation of transcript-level QTLs

The eQTL Catalogue is an open database of uniformly processed human molecular quantitative trait loci (QTLs). We are continuously updating the resource to further increase its utility for interpreting genetic associations with complex traits. Over the past two years, we have increased the number of uniformly processed studies from 21 to 31 and added X chromosome QTLs for 19 compatible studies. We have also implemented Leafcutter to directly identify splice-junction usage QTLs in all RNA sequencing datasets. Finally, to improve the interpretability of transcript-level QTLs, we have developed static QTL coverage plots that visualise the association between the genotype and average RNA sequencing read coverage in the region for all 1.7 million fine mapped associations. To illustrate the utility of these updates to the eQTL Catalogue, we performed colocalisation analysis between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. Although most GWAS loci colocalised both with eQTLs and transcript-level QTLs, we found that visual inspection could sometimes be used to distinguish primary splicing QTLs from those that appear to be secondary consequences of large-effect gene expression QTLs. While these visually confirmed primary splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases.

Global endometrial DNA methylation analysis reveals insights into mQTL regulation and associated endometriosis disease risk and endometrial function

Endometriosis is a leading cause of pain and infertility affecting millions of women globally. Herein, we characterize variation in DNA methylation (DNAm) and its association with menstrual cycle phase, endometriosis, and genetic variants through analysis of genotype data and methylation in endometrial samples from 984 deeply-phenotyped participants. We estimate that 15.4% of the variation in endometriosis is captured by DNAm and identify significant differences in DNAm profiles associated with stage III/IV endometriosis, endometriosis sub-phenotypes and menstrual cycle phase, including opening of the window for embryo implantation. Menstrual cycle phase was a major source of DNAm variation suggesting cellular and hormonally-driven changes across the cycle can regulate genes and pathways responsible for endometrial physiology and function. DNAm quantitative trait locus (mQTL) analysis identified 118,185 independent cis-mQTLs including 51 associated with risk of endometriosis, highlighting candidate genes contributing to disease risk. Our work provides functional evidence for epigenetic targets contributing to endometriosis risk and pathogenesis. Data generated serve as a valuable resource for understanding tissue-specific effects of methylation on endometrial biology in health and disease.

Single-cell transcriptomic profiling of human pancreatic islets reveals genes responsive to glucose exposure over 24 hours

Disruption of pancreatic islet function and glucose homeostasis can lead to the development of sustained hyperglycemia, beta cell glucotoxicity, and ultimately type 2 diabetes (T2D). In this study, we sought to explore the effects of hyperglycemia on human pancreatic islet (HPI) gene expression by exposing HPIs from two donors to low (2.8mM) and high (15.0mM) glucose concentrations over 24 hours, assaying the transcriptome at seven time points using single-cell RNA sequencing (scRNA-seq). We modeled time as both a discrete and continuous variable to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. Across all cell types, we identified 1,528 genes associated with time, 1,185 genes associated with glucose exposure, and 845 genes associated with interaction effects between time and glucose. We clustered differentially expressed genes across cell types and found 347 modules of genes with similar expression patterns across time and glucose conditions, including two beta cell modules enriched in genes associated with T2D. Finally, by integrating genomic features from this study and genetic summary statistics for T2D and related traits, we nominate 363 candidate effector genes that may underlie genetic associations for T2D and related traits.

QTL mapping of human retina DNA methylation identifies 87 gene-epigenome interactions in age-related macular degeneration

DNA methylation (DNAm) provides a crucial epigenetic mark linking genetic variations to environmental influence. We analyzed array-based DNAm profiles of 160 human retinas with co-measured RNA-seq and > 8 million genetic variants, uncovering sites of genetic regulation in cis (37,453 mQTLs and 12,505 eQTLs) and 13,747 eQTMs (DNAm loci affecting gene expression), with over one-third specific to the retina. mQTLs and eQTMs show non-random distribution and enrichment of biological processes related to synapse, mitochondria, and catabolism. Summary data-based Mendelian randomization and colocalization analyses identify 87 target genes where methylation and gene-expression changes likely mediate the genotype effect on age-related macular degeneration (AMD). Integrated pathway analysis reveals epigenetic regulation of immune response and metabolism including the glutathione pathway and glycolysis. Our study thus defines key roles of genetic variations driving methylation changes, prioritizes epigenetic control of gene expression, and suggests frameworks for regulation of AMD pathology by genotype-environment interaction in retina.

Genome-wide association study and functional characterization identifies candidate genes for insulin-stimulated glucose uptake

Distinct tissue-specific mechanisms mediate insulin action in fasting and postprandial states. Previous genetic studies have largely focused on insulin resistance in the fasting state, where hepatic insulin action dominates. Here we studied genetic variants influencing insulin levels measured 2 h after a glucose challenge in >55,000 participants from three ancestry groups. We identified ten new loci (P < 5 × 10-8) not previously associated with postchallenge insulin resistance, eight of which were shown to share their genetic architecture with type 2 diabetes in colocalization analyses. We investigated candidate genes at a subset of associated loci in cultured cells and identified nine candidate genes newly implicated in the expression or trafficking of GLUT4, the key glucose transporter in postprandial glucose uptake in muscle and fat. By focusing on postprandial insulin resistance, we highlighted the mechanisms of action at type 2 diabetes loci that are not adequately captured by studies of fasting glycemic traits.

Systematic visualisation of molecular QTLs reveals variant mechanisms at GWAS loci

Splicing quantitative trait loci (QTLs) have been implicated as a common mechanism underlying complex trait associations. However, utilising splicing QTLs in target discovery and prioritisation has been challenging due to extensive data normalisation which often renders the direction of the genetic effect as well as its magnitude difficult to interpret. This is further complicated by the fact that strong expression QTLs often manifest as weak splicing QTLs and vice versa, making it difficult to uniquely identify the underlying molecular mechanism at each locus. We find that these ambiguities can be mitigated by visualising the association between the genotype and average RNA sequencing read coverage in the region. Here, we generate these QTL coverage plots for 1.7 million molecular QTL associations in the eQTL Catalogue identified with five quantification methods. We illustrate the utility of these QTL coverage plots by performing colocalisation between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. We find that while visually confirmed splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases. All our association summary statistics and QTL coverage plots are freely available at https://www.ebi.ac.uk/eqtl/.

Exploiting the mediating role of the metabolome to unravel transcript-to-phenotype associations

Despite the success of genome-wide association studies (GWASs) in identifying genetic variants associated with complex traits, understanding the mechanisms behind these statistical associations remains challenging. Several methods that integrate methylation, gene expression, and protein quantitative trait loci (QTLs) with GWAS data to determine their causal role in the path from genotype to phenotype have been proposed. Here, we developed and applied a multi-omics Mendelian randomization (MR) framework to study how metabolites mediate the effect of gene expression on complex traits. We identified 216 transcript-metabolite-trait causal triplets involving 26 medically relevant phenotypes. Among these associations, 58% were missed by classical transcriptome-wide MR, which only uses gene expression and GWAS data. This allowed the identification of biologically relevant pathways, such as between ANKH and calcium levels mediated by citrate levels and SLC6A12 and serum creatinine through modulation of the levels of the renal osmolyte betaine. We show that the signals missed by transcriptome-wide MR are found, thanks to the increase in power conferred by integrating multiple omics layer. Simulation analyses show that with larger molecular QTL studies and in case of mediated effects, our multi-omics MR framework outperforms classical MR approaches designed to detect causal relationships between single molecular traits and complex phenotypes.

Exome-Wide Association Study of Competitive Performance in Elite Athletes

The aim of the study was to identify genetic variants associated with personal best scores in Turkish track and field athletes and to compare allelic frequencies between sprint/power and endurance athletes and controls using a whole-exome sequencing (WES) approach, followed by replication studies in independent cohorts. The discovery phase involved 60 elite Turkish athletes (31 sprint/power and 29 endurance) and 20 ethnically matched controls. The replication phase involved 1132 individuals (115 elite Russian sprinters, 373 elite Russian endurance athletes (of which 75 athletes were with VO2max measurements), 209 controls, 148 Russian and 287 Finnish individuals with muscle fiber composition and cross-sectional area (CSA) data). None of the single nucleotide polymorphisms (SNPs) reached an exome-wide significance level (p < 2.3 × 10−7) in genotype–phenotype and case–control studies of Turkish athletes. However, of the 53 nominally (p < 0.05) associated SNPs, four functional variants were replicated. The SIRT1 rs41299232 G allele was significantly over-represented in Turkish (p = 0.047) and Russian (p = 0.018) endurance athletes compared to sprint/power athletes and was associated with increased VO2max (p = 0.037) and a greater proportion of slow-twitch muscle fibers (p = 0.035). The NUP210 rs2280084 A allele was significantly over-represented in Turkish (p = 0.044) and Russian (p = 0.012) endurance athletes compared to sprint/power athletes. The TRPM2 rs1785440 G allele was significantly over-represented in Turkish endurance athletes compared to sprint/power athletes (p = 0.034) and was associated with increased VO2max (p = 0.008). The AGRN rs4074992 C allele was significantly over-represented in Turkish sprint/power athletes compared to endurance athletes (p = 0.037) and was associated with a greater CSA of fast-twitch muscle fibers (p = 0.024). In conclusion, we present the first WES study of athletes showing that this approach can be used to identify novel genetic markers associated with exercise- and sport-related phenotypes.

Effect of tissue-grouped regulatory variants associated to type 2 diabetes in related secondary outcomes

Genome-wide association studies have identified over five hundred loci that contribute to variation in type 2 diabetes (T2D), an established risk factor for many diseases. However, the mechanisms and extent through which these loci contribute to subsequent outcomes remain elusive. We hypothesized that combinations of T2D-associated variants acting on tissue-specific regulatory elements might account for greater risk for tissue-specific outcomes, leading to diversity in T2D disease progression. We searched for T2D-associated variants acting on regulatory elements and expression quantitative trait loci (eQTLs) in nine tissues. We used T2D tissue-grouped variant sets as genetic instruments to conduct 2-Sample Mendelian Randomization (MR) in ten related outcomes whose risk is increased by T2D using the FinnGen cohort. We performed PheWAS analysis to investigate whether the T2D tissue-grouped variant sets had specific predicted disease signatures. We identified an average of 176 variants acting in nine tissues implicated in T2D, and an average of 30 variants acting on regulatory elements that are unique to the nine tissues of interest. In 2-Sample MR analyses, all subsets of regulatory variants acting in different tissues were associated with increased risk of the ten secondary outcomes studied on similar levels. No tissue-grouped variant set was associated with an outcome significantly more than other tissue-grouped variant sets. We did not identify different disease progression profiles based on tissue-specific regulatory and transcriptome information. Bigger sample sizes and other layers of regulatory information in critical tissues may help identify subsets of T2D variants that are implicated in certain secondary outcomes, uncovering system-specific disease progression.

Multi-Omics Studies in Historically Excluded Populations: The Road to Equity

Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non-coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi-omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non-coding region, other machine learning and omics approaches have been developed to identify the potential causative single-nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome-wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome-wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi-omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.

Diversity and Differential Expression of MicroRNAs in the Human Skeletal Muscle with Distinct Fiber Type Composition

The ratio of fast- and slow-twitch fibers in human skeletal muscle is variable and largely determined by genetic factors. In this study, we investigated the contribution of microRNA (miRNA) in skeletal muscle fiber type composition. The study involved biopsy samples of the vastus lateralis muscle from 24 male participants with distinct fiber type ratios. The miRNA study included samples from five endurance athletes and five power athletes with the predominance of slow-twitch (61.6-72.8%) and fast-twitch (69.3-80.7%) fibers, respectively. Total and small RNA were extracted from tissue samples. Total RNA sequencing (N = 24) revealed 352 differentially expressed genes between the groups with the predominance of fast- and slow-twitch muscle fibers. Small RNA sequencing showed upregulation of miR-206, miR-501-3p and miR-185-5p, and downregulation of miR-499a-5p and miR-208-5p in the group of power athletes with fast-twitch fiber predominance. Two miRtronic miRNAs, miR-208b-3p and miR-499a-5p, had strong correlations in expression with their host genes (MYH7 and MYH7B, respectively). Correlations between the expression of miRNAs and their experimentally validated messenger RNA (mRNA) targets were calculated, and 11 miRNA-mRNA interactions with strong negative correlations were identified. Two of them belonged to miR-208b-3p and miR-499a-5p, indicating their regulatory links with the expression of CDKN1A and FOXO4, respectively.

Human pancreatic islet microRNAs implicated in diabetes and related traits by large-scale genetic analysis

Genetic studies have identified ≥240 loci associated with the risk of type 2 diabetes (T2D), yet most of these loci lie in non-coding regions, masking the underlying molecular mechanisms. Recent studies investigating mRNA expression in human pancreatic islets have yielded important insights into the molecular drivers of normal islet function and T2D pathophysiology. However, similar studies investigating microRNA (miRNA) expression remain limited. Here, we present data from 63 individuals, the largest sequencing-based analysis of miRNA expression in human islets to date. We characterized the genetic regulation of miRNA expression by decomposing the expression of highly heritable miRNAs into cis- and trans-acting genetic components and mapping cis-acting loci associated with miRNA expression [miRNA-expression quantitative trait loci (eQTLs)]. We found i) 84 heritable miRNAs, primarily regulated by trans-acting genetic effects, and ii) 5 miRNA-eQTLs. We also used several different strategies to identify T2D-associated miRNAs. First, we colocalized miRNA-eQTLs with genetic loci associated with T2D and multiple glycemic traits, identifying one miRNA, miR-1908, that shares genetic signals for blood glucose and glycated hemoglobin (HbA1c). Next, we intersected miRNA seed regions and predicted target sites with credible set SNPs associated with T2D and glycemic traits and found 32 miRNAs that may have altered binding and function due to disrupted seed regions. Finally, we performed differential expression analysis and identified 14 miRNAs associated with T2D status-including miR-187-3p, miR-21-5p, miR-668, and miR-199b-5p-and 4 miRNAs associated with a polygenic score for HbA1c levels-miR-216a, miR-25, miR-30a-3p, and miR-30a-5p.

Systemic interindividual epigenetic variation in humans is associated with transposable elements and under strong genetic control

BACKGROUND

Genetic variants can modulate phenotypic outcomes via epigenetic intermediates, for example at methylation quantitative trait loci (mQTL). We present the first large-scale assessment of mQTL at human genomic regions selected for interindividual variation in CpG methylation, which we call correlated regions of systemic interindividual variation (CoRSIVs). These can be assayed in blood DNA and do not reflect interindividual variation in cellular composition.

RESULTS

We use target-capture bisulfite sequencing to assess DNA methylation at 4086 CoRSIVs in multiple tissues from each of 188 donors in the NIH Gene-Tissue Expression (GTEx) program. At CoRSIVs, DNA methylation in peripheral blood correlates with methylation and gene expression in internal organs. We also discover unprecedented mQTL at these regions. Genetic influences on CoRSIV methylation are extremely strong (median R²=0.76), cumulatively comprising over 70-fold more human mQTL than detected in the most powerful previous study. Moreover, mQTL beta coefficients at CoRSIVs are highly skewed (i.e., the major allele predicts higher methylation). Both surprising findings are independently validated in a cohort of 47 non-GTEx individuals. Genomic regions flanking CoRSIVs show long-range enrichments for LINE-1 and LTR transposable elements; the skewed beta coefficients may therefore reflect evolutionary selection of genetic variants that promote their methylation and silencing. Analyses of GWAS summary statistics show that mQTL polymorphisms at CoRSIVs are associated with metabolic and other classes of disease.

CONCLUSIONS

A focus on systemic interindividual epigenetic variants, clearly enhanced in mQTL content, should likewise benefit studies attempting to link human epigenetic variation to the risk of disease.

DNA methylation QTL mapping across diverse human tissues provides molecular links between genetic variation and complex traits

Studies of DNA methylation (DNAm) in solid human tissues are relatively scarce; tissue-specific characterization of DNAm is needed to understand its role in gene regulation and its relevance to complex traits. We generated array-based DNAm profiles for 987 human samples from the Genotype-Tissue Expression (GTEx) project, representing 9 tissue types and 424 subjects. We characterized methylome and transcriptome correlations (eQTMs), genetic regulation in cis (mQTLs and eQTLs) across tissues and e/mQTLs links to complex traits. We identified mQTLs for 286,152 CpG sites, many of which (>5%) show tissue specificity, and mQTL colocalizations with 2,254 distinct GWAS hits across 83 traits. For 91% of these loci, a candidate gene link was identified by integration of functional maps, including eQTMs, and/or eQTL colocalization, but only 33% of loci involved an eQTL and mQTL present in the same tissue type. With this DNAm-focused integrative analysis, we contribute to the understanding of molecular regulatory mechanisms in human tissues and their impact on complex traits.

Genome-Wide Association Study Identifies CDKN1A as a Novel Locus Associated with Muscle Fiber Composition

Muscle fiber composition is associated with physical performance, with endurance athletes having a high proportion of slow-twitch muscle fibers compared to power athletes. Approximately 45% of muscle fiber composition is heritable, however, single nucleotide polymorphisms (SNP) underlying inter-individual differences in muscle fiber types remain largely unknown. Based on three whole genome SNP datasets, we have shown that the rs236448 A allele located near the cyclin-dependent kinase inhibitor 1A (CDKN1A) gene was associated with an increased proportion of slow-twitch muscle fibers in Russian (n = 151; p = 0.039), Finnish (n = 287; p = 0.03), and Japanese (n = 207; p = 0.008) cohorts (meta-analysis: p = 7.9 × 10−5. Furthermore, the frequency of the rs236448 A allele was significantly higher in Russian (p = 0.045) and Japanese (p = 0.038) elite endurance athletes compared to ethnically matched power athletes. On the contrary, the C allele was associated with a greater proportion of fast-twitch muscle fibers and a predisposition to power sports. CDKN1A participates in cell cycle regulation and is suppressed by the miR-208b, which has a prominent role in the activation of the slow myofiber gene program. Bioinformatic analysis revealed that the rs236448 C allele was associated with increased CDKN1A expression in whole blood (p = 8.5 × 10−15) and with greater appendicular lean mass (p = 1.2 × 10−5), whereas the A allele was associated with longer durations of exercise (p = 0.044) reported amongst the UK Biobank cohort. Furthermore, the expression of CDKN1A increased in response to strength (p < 0.0001) or sprint (p = 0.00035) training. Accordingly, we found that CDKN1A expression is significantly (p = 0.002) higher in the m. vastus lateralis of strength athletes compared to endurance athletes and is positively correlated with the percentage of fast-twitch muscle fibers (p = 0.018). In conclusion, our data suggest that the CDKN1A rs236448 SNP may be implicated in the determination of muscle fiber composition and may affect athletic performance.

Elucidating the genetic architecture of DNA methylation to identify promising molecular mechanisms of disease

DNA methylation commonly occurs at cytosine-phosphate-guanine sites (CpGs) that can serve as biomarkers for many diseases. We analyzed whole genome sequencing data to identify DNA methylation quantitative trait loci (mQTLs) in 4126 Framingham Heart Study participants. Our mQTL mapping identified 94,362,817 cis-mQTLvariant-CpG pairs (for 210,156 unique autosomal CpGs) at P < 1e-7 and 33,572,145 trans-mQTL variant-CpG pairs (for 213,606 unique autosomal CpGs) at P < 1e-14. Using cis-mQTL variants for 1258 CpGs associated with seven cardiovascular disease (CVD) risk factors, we found 104 unique CpGs that colocalized with at least one CVD trait. For example, cg11554650 (PPP1R18) colocalized with type 2 diabetes, and was driven by a single nucleotide polymorphism (rs2516396). We performed Mendelian randomization (MR) analysis and demonstrated 58 putatively causal relations of CVD risk factor-associated CpGs to one or more risk factors (e.g., cg05337441 [APOB] with LDL; MR P = 1.2e-99, and 17 causal associations with coronary artery disease (e.g. cg08129017 [SREBF1] with coronary artery disease; MR P = 5e-13). We also showed that three CpGs, e.g., cg14893161 (PM20D1), are putatively causally associated with COVID-19 severity. To assist in future analyses of the role of DNA methylation in disease pathogenesis, we have posted a comprehensive summary data set in the National Heart, Lung, and Blood Institute's BioData Catalyst.

Colocalization of Gene Expression and DNA Methylation with Genetic Risk Variants Supports Functional Roles of MUC5B and DSP in Idiopathic Pulmonary Fibrosis

Rationale: Common genetic variants have been associated with idiopathic pulmonary fibrosis (IPF). Objectives: To determine functional relevance of the 10 IPF-associated common genetic variants we previously identified. Methods: We performed expression quantitative trait loci (eQTL) and methylation quantitative trait loci (mQTL) mapping, followed by co-localization of eQTL and mQTL with genetic association signals and functional validation by luciferase reporter assays. Illumina multi-ethnic genotyping arrays, mRNA sequencing, and Illumina 850k methylation arrays were performed on lung tissue of participants with IPF (234 RNA and 345 DNA samples) and non-diseased controls (188 RNA and 202 DNA samples). Measurements and Main Results: Focusing on genetic variants within 10 IPF-associated genetic loci, we identified 27 eQTLs in controls and 24 eQTLs in cases (false-discovery-rate-adjusted P < 0.05). Among these signals, we identified associations of lead variants rs35705950 with expression of MUC5B and rs2076295 with expression of DSP in both cases and controls. mQTL analysis identified CpGs in gene bodies of MUC5B (cg17589883) and DSP (cg08964675) associated with the lead variants in these two loci. We also demonstrated strong co-localization of eQTL/mQTL and genetic signal in MUC5B (rs35705950) and DSP (rs2076295). Functional validation of the mQTL in MUC5B using luciferase reporter assays demonstrates that the CpG resides within a putative internal repressor element. Conclusions: We have established a relationship of the common IPF genetic risk variants rs35705950 and rs2076295 with respective changes in MUC5B and DSP expression and methylation. These results provide additional evidence that both MUC5B and DSP are involved in the etiology of IPF.

QTLbase2: an enhanced catalog of human quantitative trait loci on extensive molecular phenotypes

Deciphering the fine-scale molecular mechanisms that shape the genetic effects at disease-associated loci from genome-wide association studies (GWAS) remains challenging. The key avenue is to identify the essential molecular phenotypes that mediate the causal variant and disease under particular biological conditions. Therefore, integrating GWAS signals with context-specific quantitative trait loci (QTLs) (such as different tissue/cell types, disease states, and perturbations) from extensive molecular phenotypes would present important strategies for full understanding of disease genetics. Via persistent curation and systematic data processing of large-scale human molecular trait QTLs (xQTLs), we updated our previous QTLbase database (now QTLbase2, http://mulinlab.org/qtlbase) to comprehensively analyze and visualize context-specific QTLs across 22 molecular phenotypes and over 95 tissue/cell types. Overall, the resource features the following major updates and novel functions: (i) 960 more genome-wide QTL summary statistics from 146 independent studies; (ii) new data for 10 previously uncompiled QTL types; (iii) variant query scope expanded to fit 195 QTL datasets based on whole-genome sequencing; (iv) supports filtering and comparison of QTLs for different biological conditions, such as stimulation types and disease states; (v) a new linkage disequilibrium viewer to facilitate variant prioritization across tissue/cell types and QTL types.

Quantitative trait locus (xQTL) approaches identify risk genes and drug targets from human non-coding genomes

Advances and reduction of costs in various sequencing technologies allow for a closer look at variations present in the non-coding regions of the human genome. Correlating non-coding variants with large-scale multi-omic data holds the promise not only of a better understanding of likely causal connections between non-coding DNA and expression of traits but also identifying potential disease-modifying medicines. Genome-phenome association studies have created large datasets of DNA variants that are associated with multiple traits or diseases, such as Alzheimer's disease; yet, the functional consequences of variants, in particular of non-coding variants, remain largely unknown. Recent advances in functional genomics and computational approaches have led to the identification of potential roles of DNA variants, such as various quantitative trait locus (xQTL) techniques. Multi-omic assays and analytic approaches toward xQTL have identified links between genetic loci and human transcriptomic, epigenomic, proteomic and metabolomic data. In this review, we first discuss the recent development of xQTL from multi-omic findings. We then highlight multimodal analysis of xQTL and genetic data for identification of risk genes and drug targets using Alzheimer's disease as an example. We finally discuss challenges and future research directions (e.g. artificial intelligence) for annotation of non-coding variants in complex diseases.

Spatiotemporal specificity of correlated DNA methylation and gene expression pairs across different human tissues and stages of brain development

DNA methylation (DNAm) that occurs on promoter regions is primarily considered to repress gene expression. Previous studies indicated that DNAm could also show positive correlations with gene expression. Both DNAm and gene expression profiles are known to be tissue- and development-specific. This study aims to investigate how DNAm and gene expression are coordinated across different human tissues and developmental stages, as well as the biological significance of such correlations. By analyzing 2,239 samples with both DNAm and gene expression data in the same human subjects obtained from six published datasets, we evaluated the correlations between gene and CpG pairs (GCPs) at cis-regions and compared significantly correlated GCPs (cGCPs) across different tissues and brains at different age groups. A total of 37,363 cGCPs was identified in the six datasets; approximately 38% of the cGCPs were positively correlated. The majority (>90%) of cGCPs was tissue- or development-specific. We also observed that the correlation direction can be opposite in different tissues and ages. Further analysis highlights the importance of cGCPs for their cellular functions and potential roles in complex traits and human diseases. For instance, the early developmental brain possessed a highly unique set of cGCPs that were associated with neurogenesis and psychiatric disorders. By assessing the epigenetic factors involved in cGCPs, we discovered novel regulatory mechanisms of positive cGCPs distinct from negative cGCPs, which were related to multiple factors, such as H3K27me3, CTCF, and JARD2. The catalogue of cGCPs compiled can be used to guide functional interpretation of genetic and epigenetic studies.

Sex- and age-dependent genetics of longevity in a heterogeneous mouse population

DNA variants that modulate life span provide insight into determinants of health, disease, and aging. Through analyses in the UM-HET3 mice of the Interventions Testing Program (ITP), we detected a sex-independent quantitative trait locus (QTL) on chromosome 12 and identified sex-specific QTLs, some of which we detected only in older mice. Similar relations between life history and longevity were uncovered in mice and humans, underscoring the importance of early access to nutrients and early growth. We identified common age- and sex-specific genetic effects on gene expression that we integrated with model organism and human data to create a hypothesis-building interactive resource of prioritized longevity and body weight genes. Finally, we validated Hipk1, Ddost, Hspg2, Fgd6, and Pdk1 as conserved longevity genes using Caenorhabditis elegans life-span experiments.

Profiling Analysis of N6-Methyladenosine mRNA Methylation Reveals Differential m6A Patterns during the Embryonic Skeletal Muscle Development of Ducks

Simple Summary

Recent studies show that N6-methyladenosine (m6A) modification, the most common RNA chemical modification, influences the modification, processing, transport, and translation of RNA. N6-methyladenosine is an epigenetic modification that influences skeletal myogenesis and skeletal muscle development. However, the N6-methyladenosine modification profile and its function during poultry muscle development is unclear, and there is only one report about m6A modification in ducks, which focuses on duck hepatitis A virus infection. Here, we compared the m6A modification profiles between E13 (embryonic day 13) and E19 (embryonic day 19) in duck breast muscle differentiation using MeRIP-seq, and evaluated the expression profile of the methyl transferase METTL14 and its cofactors during breast muscle development. This is the first study of N6-methyladenosine modification patterns in duck muscle tissue. The current study not only elucidates the regulation mechanisms of duck skeletal muscle development, but also lays the groundwork for studying the role of RNA modification in poultry muscle development.

Abstract

N6-Methyladenosine is a reversible epigenetic modification that influences muscle development. However, the m6A modification profile during poultry skeletal muscle development is poorly understood. Here, we utilized m6A-specific methylated RNA immunoprecipitation sequencing to identify m6A sites during two stages of breast muscle development in ducks: embryonic days 13 (E13) and E19. MeRIP-seq detected 19,024 and 18,081 m6A peaks in the E13 and E19 groups, respectively. Similarly to m6A distribution in mammalian transcripts, our results revealed GGACU as the main m6A motif in duck breast muscle; they also revealed that m6A peaks are mainly enriched near the stop codons. In addition, motif sequence analysis and gene expression analysis demonstrated that m6A modification in duck embryo skeletal muscles may be mediated by the methyltransferase-like 14. GO and KEGG analysis showed that m6A peaks containing genes at E19 were mainly enriched in muscle-differentiation- and muscle-growth-related pathways, whereas m6A peaks containing genes in E13 were mainly enriched in embryonic development and cell proliferation pathways. Combined analysis of MeRIP-seq and RNA-seq showed that the mRNA expression may be affected by m6A modification. Moreover, qRT-PCR analysis of the expression of METTL14 and its cofactors (WTAP, ZC3H13, RBM15 and VIRMA) during duck embryonic skeletal muscle development in breast and leg muscle samples revealed a significant downward trend as the developmental age progressed. Our results demonstrated that m6A mRNA methylation modifications control muscle development in ducks. This is the first study of m6A modification patterns in duck muscle tissue development, and it lays the foundation for the study of the effects of RNA modification on poultry skeletal muscle development.

The regulation of LncRNA GTL2 expression by DNA methylation during sheep skeletal muscle development

DNA methylation has crucial roles in regulating the expression of genes involved in skeletal muscle development. However, the DNA methylation pattern of lncRNA during sheep skeletal muscle development remains unclear. This study investigated previous WGBS and LncRNA data in skeletal muscle of sheep (fetus and adult). We then focused on LncRNA GTL2, which is differentially expressed in skeletal muscle and has multiple DMRs. We found that the expression level of GTL2 decreased with age. GTL2 DMRs methylation levels were significantly higher in adult muscle than in fetal muscle. After 5AZA treatment, GTL2 expression was significantly increased in a dose-dependent manner.The dCas9-DNMT3A-sgRNA significantly reduced the expression level of GTL2 in cells, but increased GTL2 DMR methylation levels. The above studies indicate that dCas9-DNMT3A can effectively increase the methylation level in the DMR region of GTL2, the expression level of GTL2 is regulated by DNA methylation during muscle development.

eQTLs as causal instruments for the reconstruction of hormone linked gene networks

Hormones act within in highly dynamic systems and much of the phenotypic response to variation in hormone levels is mediated by changes in gene expression. The increase in the number and power of large genetic association studies has led to the identification of hormone linked genetic variants. However, the biological mechanisms underpinning the majority of these loci are poorly understood. The advent of affordable, high throughput next generation sequencing and readily available transcriptomic databases has shown that many of these genetic variants also associate with variation in gene expression levels as expression Quantitative Trait Loci (eQTLs). In addition to further dissecting complex genetic variation, eQTLs have been applied as tools for causal inference. Many hormone networks are driven by transcription factors, and many of these genes can be linked to eQTLs. In this mini-review, we demonstrate how causal inference and gene networks can be used to describe the impact of hormone linked genetic variation upon the transcriptome within an endocrinology context.

Association of muscle fiber type with measures of obesity: A systematic review

Obesity derives from an extended period of positive energy imbalance due to a complex interplay of environmental and biological factors. Muscle fiber type and physiology have been hypothesized to affect metabolism and energy expenditure and thus to affect an individual's propensity to gain weight. However, there have been conflicting reports regarding a relationship between muscle fiber type and obesity. Here, we systematically reviewed literature investigating this topic from PubMed, Web of Science, and EMBASE. Of these, 32 articles were included in our final review and analysis. Most studies (22/32) reported a significant relationship between muscle fiber-type proportion and a measure of obesity. Overall, there was a significant negative relationship between the proportion of type I fibers and body mass index (BMI) and a significant positive relationship between the proportion of type IIX fibers and BMI. Moreover, between-group comparisons indicated a greater prevalence of type IIX fibers and a lower prevalence of type I fibers in patients living with obesity relative to lean individuals. These significant relationships were confirmed in a meta-analysis of these data. The causal nature of these associations remains to be evaluated.

Anti-cancer therapy is associated with long-term epigenomic changes in childhood cancer survivors

BACKGROUND

Childhood cancer survivors (CCS) exhibit significantly increased chronic diseases and premature death. Abnormalities in DNA methylation are associated with development of chronic diseases and reduced life expectancy. We investigated the hypothesis that anti-cancer treatments are associated with long-term DNA methylation changes that could be key drivers of adverse late health effects.

METHODS

Genome-wide DNA methylation was assessed using MethylationEPIC arrays in paired samples (before/after therapy) from 32 childhood cancer patients. Separately, methylation was determined in 32 samples from different adult CCS (mean 22-years post-diagnosis) and compared with cancer-free controls (n = 284).

RESULTS

Widespread DNA methylation changes were identified post-treatment in childhood cancer patients, including 146 differentially methylated regions (DMRs), which were consistently altered in the 32 post-treatment samples. Analysis of adult CCS identified matching methylation changes at 107/146 of the DMRs, suggesting potential long-term retention of post-therapy changes. Adult survivors also exhibited epigenetic age acceleration, independent of DMR methylation. Furthermore, altered methylation at the DUSP6 DMR was significantly associated with early mortality, suggesting altered methylation may be prognostic for some late adverse health effects in CCS.

CONCLUSIONS

These novel methylation changes could serve as biomarkers for assessing normal cell toxicity in ongoing treatments and predicting long-term health outcomes in CCS.

Epigenetics of type 2 diabetes mellitus and weight change - a tool for precision medicine?

Pioneering studies performed over the past few decades demonstrate links between epigenetics and type 2 diabetes mellitus (T2DM), the metabolic disorder with the most rapidly increasing prevalence in the world. Importantly, these studies identified epigenetic modifications, including altered DNA methylation, in pancreatic islets, adipose tissue, skeletal muscle and the liver from individuals with T2DM. As non-genetic factors that affect the risk of T2DM, such as obesity, unhealthy diet, physical inactivity, ageing and the intrauterine environment, have been associated with epigenetic modifications in healthy individuals, epigenetics probably also contributes to T2DM development. In addition, genetic factors associated with T2DM and obesity affect the epigenome in human tissues. Notably, causal mediation analyses found DNA methylation to be a potential mediator of genetic associations with metabolic traits and disease. In the past few years, translational studies have identified blood-based epigenetic markers that might be further developed and used for precision medicine to help patients with T2DM receive optimal therapy and to identify patients at risk of complications. This Review focuses on epigenetic mechanisms in the development of T2DM and the regulation of body weight in humans, with a special focus on precision medicine.

Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease

More than 800 million people suffer from kidney disease, yet the mechanism of kidney dysfunction is poorly understood. In the present study, we define the genetic association with kidney function in 1.5 million individuals and identify 878 (126 new) loci. We map the genotype effect on the methylome in 443 kidneys, transcriptome in 686 samples and single-cell open chromatin in 57,229 kidney cells. Heritability analysis reveals that methylation variation explains a larger fraction of heritability than gene expression. We present a multi-stage prioritization strategy and prioritize target genes for 87% of kidney function loci. We highlight key roles of proximal tubules and metabolism in kidney function regulation. Furthermore, the causal role of SLC47A1 in kidney disease is defined in mice with genetic loss of Slc47a1 and in human individuals carrying loss-of-function variants. Our findings emphasize the key role of bulk and single-cell epigenomic information in translating genome-wide association studies into identifying causal genes, cellular origins and mechanisms of complex traits.

LARGE-SCALE INTEGRATION OF DNA METHYLATION AND GENE EXPRESSION ARRAY PLATFORMS IDENTIFIES BOTH cis AND trans RELATIONSHIPS

Although epigenome-wide association studies (EWAS) have been successful in identifying DNA methylation (DNAm) patterns associated with disease states, any further characterization of etiologic mechanisms underlying disease remains elusive. This knowledge gap does not originate from a lack of DNAm–trait associations, but rather stems from study design issues that affect the interpretability of EWAS results. Despite known limitations in predicting the function of a particular CpG site, most EWAS maintain the broad assumption that altered DNAm results in a concomitant change of transcription at the most proximal gene. This study integrated DNAm and gene expression (GE) measurements in two cohorts, the Adolescent and Young Adult Twin Study (AYATS) and the Pregnancy, Race, Environment, Genes (PREG) study, to improve the understanding of epigenomic regulatory mechanisms. CpG sites associated with GE in cis were enriched in areas of transcription factor binding and areas of intermediate-to-low CpG density. CpG sites associated with trans GE were also enriched in areas of known regulatory significance, including enhancer regions. These results highlight issues with restricting DNAm-transcript annotations to small genomic intervals and question the validity of assuming a cis DNAm–GE pathway. Based on these findings, the interpretation of EWAS results is limited in studies without multi-omic support and further research should identify genomic regions in which GE-associated DNAm is overrepresented. An in-depth characterization of GE-associated CpG sites could improve predictions of the downstream functional impact of altered DNAm and inform best practices for interpreting DNAm–trait associations generated by EWAS.